A stratified fuzzy decision-making approach for engineering procurement construction project quality evaluation with triangular fuzzy neutrosophic information

Article type: Research Article

Authors: Lei, Yong

Affiliations: Changsha Normal University, Changsha, Hunan, China | E-mail: [email protected]

Correspondence: [*] Corresponding author: Changsha Normal University, Changsha, Hunan, China. E-mail: [email protected].

Abstract: EPC (Engineering, Procedure, Construction) refers to the implementation of “procurement, construction, and design” for a certain project, which has a very similar meaning to general engineering contracting. The general contracting mode of EPC is that the construction enterprise, as the owner, contracts the construction project to the general contracting enterprise in a direct form. The engineering procurement construction project quality evaluation is looked as the multi-attribute decision-making (MADM). The triangular fuzzy neutrosophic sets (TFNSs) is more suitable for expressing uncertain information during the engineering procurement construction project quality evaluation. Grey relational analysis (GRA) method is a very active branch of grey system theory, whose basic idea is to determine whether the connections between different sequences are close based on the similarity of the geometric shapes of sequence curves. In this paper, the triangular fuzzy neutrosophic number GRA (TFNN-GRA) method is put up under triangular fuzzy neutrosophic sets (TFNSs) with completely unknown weight information. The information entropy is employed to obtain the weight values under TFNSs. Then, commenting GRA method with TFNSs, the TFNN-GRA is designed and the decision steps for MADM are constructed. Finally, a numerical example for engineering procurement construction project quality evaluation was given and some comparative analysis is employed to verify the advantages of TFNN-GRA method.

Keywords: Multiple attribute decision making (MAGDM) problems, TFNSs, GRA method, engineering procurement construction (EPC) project

DOI: 10.3233/KES-230099

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 28, no. 1, pp. 179-194, 2024